A number of industrial and non-industrial applications use error detection and/or fault diagnosis systems to determine when equipment is damaged or otherwise exhibiting an error condition. Typical model types include multivariate linear or non-linear regression models, multi-regional linear regression models (or “piece-wise linear models”), principal component analysis (PCA) or partial least square (PLS) based models, discriminant analysis, and neural network based models, to name a few. Generally speaking, in these models, historical and/or live data generated under normal equipment operating conditions are collected in order to build the model characterizing the relationship between a process input and a process output.
These models generally require a substantial amount of time to perform designed experimental tests and collect data while the equipment is online, thus impacting operating efficiencies. Conversely, collecting and analyzing historical data in an off-line manner is also time consuming and can impact operating efficiencies. If the data collection is incomplete, or if an operational aspect of the actual process is modified while in operation, subsequent operation of the process model may be ineffective because the existing process model may be incapable of distinguishing between actual errors and false positives. For example, the process model may be unable to determine a difference between an equipment error and a normal scenario when a new, but normal, process scenario is encountered that was not reflected in previous historical data, or when information regarding this normal operating condition was missed in the original process model due to human error and/or time constraints.
Variations in component quality can also have an impact on asset outputs. As a non-limiting example, in a power plant environment utilizing pulverizers, coal quality (e.g., heating value, moisture content, etc.) can vary under different conditions. As a result, it may be difficult to initially collect and model data to cover every quality scenario. In this and other examples, output deviations do not necessarily mean that the unit is operating erroneously or is in danger of failure. Because the process model data is typically the only source of failure information, existing systems are limited in potential options for improvement.